def __init__(self, teacher, dataset_path, store_path, dataset_config, best_trade_off): self.dataset_path = dataset_path self.store_path = store_path self.teacher = teacher self.dataset_config = dataset_config self.rotate = dataset_config.use_rotation self.trade_off = best_trade_off if os.path.exists(self.store_path): raise Exception("Store path already exists") else: os.makedirs(self.store_path) os.makedirs(os.path.join(self.store_path, "train")) os.makedirs(os.path.join(self.store_path, "valid")) os.makedirs(os.path.join(self.store_path, "test")) self.evaluate = util.create_simple_predictor(teacher['model'], teacher['params']) self.creator = Creator( self.dataset_path, dim=(self.dataset_config.input_dim, self.dataset_config.output_dim), preproccessing=self.dataset_config.use_preprocessing, std=self.dataset_config.dataset_std, reduce_training=self.dataset_config.reduce_training, reduce_testing=self.dataset_config.reduce_testing, reduce_validation=self.dataset_config.reduce_validation, only_mixed=self.dataset_config.only_mixed_labels, mix_ratio=self.dataset_config.mix_ratio) self.creator.load_dataset()
def __init__(self, teacher, dataset_path, store_path, dataset_config, best_trade_off): self.dataset_path = dataset_path self.store_path = store_path self.teacher = teacher self.dataset_config = dataset_config self.rotate = dataset_config.use_rotation self.trade_off = best_trade_off if os.path.exists(self.store_path): raise Exception("Store path already exists") else: os.makedirs(self.store_path) os.makedirs(os.path.join(self.store_path, "train")) os.makedirs(os.path.join(self.store_path, "valid")) os.makedirs(os.path.join(self.store_path, "test")) self.evaluate = util.create_simple_predictor(teacher['model'], teacher['params']) self.creator = Creator( self.dataset_path, dim=(self.dataset_config.input_dim, self.dataset_config.output_dim), preproccessing=self.dataset_config.use_preprocessing, std=self.dataset_config.dataset_std, reduce_training=self.dataset_config.reduce_training, reduce_testing=self.dataset_config.reduce_testing, reduce_validation=self.dataset_config.reduce_validation, only_mixed=self.dataset_config.only_mixed_labels, mix_ratio=self.dataset_config.mix_ratio ) self.creator.load_dataset()
verify, stage = get_command('-verify', default="0") stage = "stage" + stage is_tradeoff, tradeoff = get_command('-tradeoff', default="0.5") tradeoff = float(tradeoff) #Dataset path. Config used if not supplied is_alt_dataset, alt_dataset = get_command('-dataset') if is_alt_dataset: dataset_path = alt_dataset #============================================================== store = ParamStorage() teacher = store.load_params(path=teacher_location) evaluate = util.create_simple_predictor(teacher['model'], teacher['params']) if not verify: creator = Creator(pr_path, dim=(dataset_params.input_dim, dataset_params.output_dim), preproccessing=dataset_params.use_preprocessing, std=dataset_params.dataset_std, reduce_training=dataset_params.reduce_training, reduce_testing=dataset_params.reduce_testing, reduce_validation=dataset_params.reduce_validation) creator.load_dataset() data, labels = creator.sample_data(creator.train, samples, rotation=dataset_params.use_rotation)
stage = "stage" + stage is_tradeoff, tradeoff = get_command('-tradeoff', default="0.5") tradeoff = float(tradeoff) #Dataset path. Config used if not supplied is_alt_dataset, alt_dataset = get_command('-dataset') if is_alt_dataset: dataset_path = alt_dataset #============================================================== store = ParamStorage() teacher = store.load_params(path=teacher_location) evaluate = util.create_simple_predictor(teacher['model'], teacher['params']) if not verify: creator = Creator( pr_path, dim=(dataset_params.input_dim, dataset_params.output_dim), preproccessing=dataset_params.use_preprocessing, std=dataset_params.dataset_std, reduce_training=dataset_params.reduce_training, reduce_testing=dataset_params.reduce_testing, reduce_validation=dataset_params.reduce_validation ) creator.load_dataset() data, labels = creator.sample_data( creator.train,